ESTIMATING CHANGES IN FOREST COVER IN THE RUDSAR COUNTY BY USING NEURAL NETWORK AND MAXIMUM LIKELIHOOD METHODS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

FATEMI TALAB S.R.; MADANIPOUR KERMANSHAHI M.; HASHEMI S.A.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of RS and GIS for Natural Resources Year:2015 | Volume:6 | Issue:2 Page(s): 33-43

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,160

Download:

Cites:

Information Journal Paper

Title

ESTIMATING CHANGES IN FOREST COVER IN THE RUDSAR COUNTY BY USING NEURAL NETWORK AND MAXIMUM LIKELIHOOD METHODS

Author(s)

FATEMI TALAB S.R. | MADANIPOUR KERMANSHAHI M. | HASHEMI S.A. | Issue Writer Certificate

Keywords

DISCOVER THE CHANGESQ1

THE METHOD OF MAXIMUM LIKELIHOODQ3

NEURAL NETWORKSQ2

RODSAR FORESTQ2

Abstract

The acquisition of knowledge about the vegetation plays an important role in soil management. However, vegetation estimating in the usual way, including an overall assessment of the vegetation is time consuming and does not also provide accurate enough information. Therefore, remote sensing technology is a desirable way for reducing time and cost compared to other usual methods. In this study, forest cover maps were prepared using remote sensing techniques and LandSat ETM+ imagery of year 2000 and LandSat 8 of year 2013. The classification of the study area digital images was performed to prepare land use map classification using maximum likelihood and neural network with participation of different bands. The results showed that the best overall accuracy of image classification using NEURAL NETWORKS ETM+ in 2000 and LandSat 8 in 2013 was 0.95 and 0.95 respectively. It was also indicated that the kappa coefficient was estimated 0.91 and 0.91 respectively. The overall accuracy of maximum likelihood method of the collected images of 2000 and 2013 was 0.95 and 0.85, but it was 0.86 and 0.84 for Kappa statistics method. The results also showed a 1054.507 and 635.319 hectares decreasing of forest cover using neural network classification and maximum likelihood classification methods respectively. According to classification accuracy and Kappa statistics, it was observed that the accuracy and kappa coefficient of neural network classification was higher than accuracy and the Kappa coefficient of maximum likelihood method.

Cites

No record.

References

LAND USE CHANGE DETECTION USING SATELLITE IMAGERY IN TANGEH-SORKH WATERSHED OF SHIRAZ

Cite

APA: Copy

FATEMI TALAB, S.R., MADANIPOUR KERMANSHAHI, M., & HASHEMI, S.A.. (2015). ESTIMATING CHANGES IN FOREST COVER IN THE RUDSAR COUNTY BY USING NEURAL NETWORK AND MAXIMUM LIKELIHOOD METHODS. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 6(2), 33-43. SID. https://sid.ir/paper/189514/en

Vancouver: Copy

FATEMI TALAB S.R., MADANIPOUR KERMANSHAHI M., HASHEMI S.A.. ESTIMATING CHANGES IN FOREST COVER IN THE RUDSAR COUNTY BY USING NEURAL NETWORK AND MAXIMUM LIKELIHOOD METHODS. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2015;6(2):33-43. Available from: https://sid.ir/paper/189514/en

IEEE: Copy

S.R. FATEMI TALAB, M. MADANIPOUR KERMANSHAHI, and S.A. HASHEMI, “ESTIMATING CHANGES IN FOREST COVER IN THE RUDSAR COUNTY BY USING NEURAL NETWORK AND MAXIMUM LIKELIHOOD METHODS,” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 6, no. 2, pp. 33–43, 2015, [Online]. Available: https://sid.ir/paper/189514/en

Related Journal Papers